Al-Zadjali Najiba Said Hamed, Balasubaramanian Sundaravadivazhagan, Savarimuthu Charles, Rances Emanuel O
College of Computing and Information and Sciences, University of Technology and Applied Sciences, Al Mussanah, Oman.
College of Engineering and Technology, University of Technology and Applied Sciences, Al Mussanah, Oman.
Sci Rep. 2025 May 3;15(1):15521. doi: 10.1038/s41598-025-99951-4.
The rapid increase in Unmanned Aerial Vehicle (UAV) deployments has led to growing concerns about their detection and differentiation from birds, particularly in sensitive areas like airports. Existing detection systems often struggle to distinguish between UAVs and birds due to their similar flight patterns, resulting in high false positive rates and missed detections. This research presents a bio-inspired deep learning model, the Spatiotemporal Bio-Response Neural Network (STBRNN), designed to enhance the differentiation between UAVs and birds in real-time. The model consists of three core components: a Bio-Inspired Convolutional Neural Network (Bio-CNN) for spatial feature extraction, Gated Recurrent Units (GRUs) for capturing temporal motion dynamics, and a novel Bio-Response Layer that adjusts attention based on movement intensity, object proximity, and velocity consistency. The dataset used includes labeled images and videos of UAVs and birds captured in various environments, processed following YOLOv7 specifications. Extensive experiments were conducted comparing STBRNN with five state-of-the-art models, including YOLOv5, Faster R-CNN, SSD, RetinaNet, and R-FCN. The results demonstrate that STBRNN achieves superior performance across multiple metrics, with a precision of 0.984, recall of 0.964, F1 score of 0.974, and an IoU of 0.96. Additionally, STBRNN operates at an inference time of 45ms per frame, making it highly suitable for real-time applications in UAV and bird detection.
无人驾驶飞行器（UAV）部署的迅速增加引发了人们对其检测以及与鸟类区分的日益关注，尤其是在机场等敏感区域。由于无人机和鸟类的飞行模式相似，现有的检测系统常常难以区分它们，导致误报率高且存在漏检情况。本研究提出了一种受生物启发的深度学习模型，即时空生物响应神经网络（STBRNN），旨在实时增强无人机与鸟类之间的区分能力。该模型由三个核心组件组成：用于空间特征提取的受生物启发的卷积神经网络（Bio-CNN）、用于捕捉时间运动动态的门控循环单元（GRU），以及一个基于运动强度、物体接近度和速度一致性来调整注意力的新型生物响应层。所使用的数据集包括在各种环境中拍摄的带有标签的无人机和鸟类图像及视频，并按照YOLOv7规范进行处理。进行了广泛的实验，将STBRNN与五个最先进的模型进行比较，包括YOLOv5、Faster R-CNN、SSD、RetinaNet和R-FCN。结果表明，STBRNN在多个指标上均取得了卓越的性能，精确率为0.984，召回率为0.964，F1分数为0.974，交并比为0.96。此外，STBRNN每帧的推理时间为45毫秒，非常适合无人机和鸟类检测的实时应用。
